Can mutual fund managers pick stocks? Evidence from their trades prior to earnings announcements∗

Malcolm Baker

Harvard Business School and NBER mbaker@hbs.edu

Lubomir Litov

NYU Stern School of Business llitov@stern.nyu.edu

Jessica A. Wachter

University of Pennsylvania Wharton School and NBER jwachter@wharton.upenn.edu

Jeffrey Wurgler

NYU Stern School of Business jwurgler@stern.nyu.edu

September 16, 2004

Abstract

We design tests of stock-picking skill based on the subsequent earnings announcement returns of the stocks that fund managers hold and trade. This approach largely avoids the joint-hypothesis problem encountered by studies that test for skill using long-horizon returns. Consistent with skilled trading, we find that, on average, stocks that funds buy earn significantly higher returns at subsequent earnings announcements than stocks that they sell. Funds also display persistence in our event return-based metrics, and those that do well tend to have a growth objective, large size, high turnover, and use incentive fees to motivate managers.

∗ We thank Marcin Kacperczyk, Andrew Metrick, Lasse Pedersen, and Robert Stambaugh for helpful comments. We thank Christopher Blake and Russ Wermers for assistance with data. Baker gratefully acknowledges financial support from the Division of Research of the Harvard Business School.

Can mutual fund managers pick stocks? Evidence from their trades prior to earnings announcements

Abstract

We design tests of stock-picking skill based on the subsequent earnings announcement returns of the stocks that fund managers hold and trade. This approach largely avoids the joint-hypothesis problem encountered by studies that test for skill using long-horizon returns. Consistent with skilled trading, we find that, on average, stocks that funds buy earn significantly higher returns at subsequent earnings announcements than stocks that they sell. Funds also display persistence in our event return-based metrics, and those that do well tend to have a growth objective, large size, high turnover, and use incentive fees to motivate managers.

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I. Introduction

Can mutual fund managers pick stocks that earn abnormal returns? This question

has long interested financial economists due to its practical importance for investors and

the light it sheds on market efficiency. Despite the many and varied approaches taken to

address this question, a common difficulty emerges: defining risk-adjusted returns.

Portfolio performance must be adjusted for risk, and the proper adjustment is unknown.

This joint hypothesis problem, articulated by Fama (1970), clouds the interpretation of

most performance studies and has led to prolonged debate about whether fund managers

can pick stocks.1

In this paper, we introduce a new methodology to measure stock-selection ability

based on returns around earnings announcements. The core idea is to associate skill with

the tendency to hold stocks that are about to enjoy high earnings announcement returns

and to avoid stocks that are about to suffer low announcement returns. This “earnings

announcement alpha” methodology may offer both more interpretable and more powerful

tests for the existence of information-based trading.

Our approach offers several advantages. First, it largely avoids the joint-

hypothesis problem. As Brown and Warner (1985) show, inference based on daily returns

around announcement dates is relatively insensitive to the risk adjustment model. We

apply this insight to performance evaluation. Just as stock returns around earnings

announcements are mostly abnormal, regardless of the risk adjustment, a mutual fund’s

returns from holding that stock are also mostly abnormal. Second, this approach makes

1 Long-horizon studies that discuss or center on the risk-adjustment issue, reaching varying conclusions, include Lehman and Modest (1987), Elton, Gruber, Das, and Hlavka (1993), Grinblatt and Titman (1993), Malkiel (1995), Ferson and Schadt (1996), Daniel, Grinblatt, Titman, and Wermers (1997), Carhart (1997), Metrick (1999), and Pastor and Stambaugh (2002) among others.

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intensive use of the segment of returns data, returns at earnings announcements, that

contains concentrated information about a firm’s fundamentals, and hence about a fund

manager’s skill at fundamental analysis. Third, Kothari and Warner (2001) provide

simulation evidence that following trades can considerably increase power to detect skill.

We combine this source of power with the additional power that comes from focusing on

returns around important announcements.2

The data set merges mutual funds’ portfolio holdings with the respective returns

that each holding realized at its next quarterly earnings announcement. The holdings are

drawn from mandatory, periodic SEC filings which are tabulated by Thompson Financial.

These data have been used by Grinblatt and Titman (1989) and Wermers (1999), among

others. For each fund-date-holding observation in these data, we merge in the return that

that stock earned in the 3-day window around its next earnings announcement. The

sample covers 1980 through 2002 and contains 6.3 million fund-report date-holding

observations with associated earnings announcement returns.

We naturally begin the analysis by tabulating the earnings announcement returns

of fund holdings, but the cleanest and most important results involve fund trades. In

particular, for each fund, we track the subsequent earnings announcement returns of the

stocks on which it increases portfolio weight over the prior period and the stocks on

which it decreases the portfolio weight. Our main finding is that the average mutual fund

2 Previous researchers, following other sets of investors than individual mutual fund managers, have also used trading prior to earnings announcements to detect information-based trading. For instance, Seasholes (2000) examines trading by foreign investors in emerging markets; Ali, Durtschi, Lev, and Trombley (2004), and Irvine, Lipson, and Puckett (2004) examine the trading patterns of institutional investors. Ke, Huddart, and Petroni (2003) follow trading by corporate insiders; and Christophe, Ferri, and Angel (2004) follow short sellers. On the other hand, Grinblatt and Titman (1993), Chen, Jegadeesh, and Wermers (2000), and subsequent authors have studied mutual fund trades, but do not simultaneously focus on returns at earnings announcements.

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shows stock-picking skill in the sense that the subsequent earnings announcement returns

on its weight-increasing stocks is significantly higher than that of its weight-decreasing

stocks. The difference is about 12 basis points over the three-day window around the

quarterly announcement, or, multiplying by four, about 47 “annualized” basis points. The

contrast between buys that initiate a fund’s position in a stock and sells that close out a

position is even larger.

In addition to comparing the earnings announcement returns of stocks that funds

buy and sell against each other, we also benchmark buys and sells against announcement

returns earned by stocks of similar size, book-to-market, and past earnings announcement

return momentum (to control for the Bernard and Thomas (1989) positive autocorrelation

in announcement returns) in the same calendar quarter. This experiment indicates that the

average fund displays some skill in both its buying and selling behavior. That is, stocks

bought by the average fund earn significantly higher subsequent announcement returns

than matching stocks, while stocks sold earn lower returns.

Although the average fund displays evidence of skill, there are also significant

differences in the cross-section of funds. For instance, there is evidence of persistence in

the earnings announcement alphas. In addition, funds that do better are more likely to

have a growth than income style, consistent with Daniel, Grinblatt, Titman, and Wermers

(1997) and several long-horizon studies. We also find that larger funds, higher turnover

funds, and those that use incentive fees show better performance by our metrics. These

results lend important support to long-horizon studies, including Grinblatt and Titman

(1994) on turnover and Elton, Gruber, and Blake (2003) on fees. In contrast to these

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papers, our methodology allows us to connect these differences in performance to

information-based trading.

In summary, an “earnings announcement alpha” methodology allows us to

provide new evidence that active fund managers have some degree of stock-picking skill.

However, we note that this methodology, precisely because it uses only a subset of the

total returns data and a particular, well-defined notion of skill, is not suited to measuring

the total abnormal returns earned by fund managers, or to addressing whether active

mutual fund managers earn abnormal returns that are large enough to exceed the fees they

charge. (However, we find no relationship between our measures of skill and expense

ratios.) Rather, our measures should be seen as putting a lower bound on the abnormal

performance attributable to stock-picking skill—notably, this lower bound appears to be

significantly positive. More generally, our measures offer a useful complement to

traditional performance metrics.

The paper proceeds as follows. Section II presents data. Section III presents

empirical results. Section IV summarizes and concludes.

II. Data

A. Data set construction

The backbone of our data set is the mutual fund holdings data from Thomson

Financial (also known as CDA/Spectrum S12). Thomson’s main source is the portfolio

snapshot contained in the N-30D form each fund periodically files with the SEC. Prior to

1985, the SEC required each fund to report its portfolio quarterly, but starting in 1985 it

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required only semiannual reports.3 The exact report dates are set by the fund as suits its

fiscal year, and a few funds voluntarily report more often than required. At a minimum,

the Thomson data give us semiannual snapshots of all equity holdings for essentially all

mutual funds. A sample fund-report date-holding observation is as follows: Fidelity

Magellan, as of March 31, 1992, held 190,000 shares of Apple Computer. Wermers

(1999) describes this data set in detail.

We extract all fund holdings data whose report date falls between the second

quarter of 1980 and the third quarter of 2002. We then add “liquidating” observations,

which are essentially placeholders to capture recent selling activity, to represent instances

where the fund appears to have sold all of its holdings in a stock for which it reported

positive holdings at the previous report date. For example, Magellan did not report a

holding of Apple stock in its June 30, 1992 report, so we construct an observation with a

holding of zero Apple for this report date.

To these holdings data, we merge in earnings announcement dates from the

CRSP/Compustat merged industrial quarterly database. Specifically, for each fund-report

date-holding observation, we merge in the first earnings announcement date that follows

that holding’s report date. We drop observations for which we can find no earnings

announcement date within 90 days after the report date.

Next we add the stock returns around each earnings announcement. From CRSP,

we merge in the raw cumulative stock returns for the [-1,+1] trading day interval around

each announcement. We define a market-adjusted event return MAR as the raw

announcement return minus the contemporaneous return on the CRSP value-weighted

3 In February 2004, the SEC decided to return to a quarterly reporting requirement.

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market index. We also define a benchmark-adjusted event return BAR as the raw return

minus the average [-1, +1] earnings announcement return on stocks of similar book-to-

market, size, and momentum that also announced earnings in the same calendar quarter

as the holding in question. Other than the fact that (for reasons described below) we take

“momentum” here as momentum of past earnings announcement returns, not total

returns, our approach is similar to that in Daniel et al. (1997).4 We exclude fund-report

dates that do not have at least one benchmark-adjusted earnings announcement return;

our results are unchanged if we restrict attention to fund-report dates containing at least

10 or at least 20 such returns.

For a subset of the remaining observations, we can obtain fund characteristics

data. Russ Wermers and WRDS provided links between the Thomson holdings data and

the CRSP mutual fund database. Wermers (2000) describes how those links are made.

Then, from the CRSP mutual fund data, we take investment objective codes from

CDA/Wiesenberger and Standard & Poor’s, as well as total net assets, turnover, and

expense ratios.5 Christopher Blake shared data on incentive fees as studied in Elton,

4 Specifically, we form the value-weighted average earnings announcement return for each of 125 benchmark portfolios (5x5x5 sorts on book-to-market, size, and earnings announcement return momentum) each calendar quarter. Book-to-market is defined following Fama and French (1995). Market value of equity is computed using the CRSP monthly file as the close times shares outstanding as of December of the calendar year preceding the fiscal year data. The book-to-market ratio is then matched from fiscal years ending in year (t-1) to earnings announcement returns starting in July of year (t) and from fiscal years ending in (t-2) to earnings announcement returns in January through June of year (t). Size is matched from June of calendar year (t) to returns starting in July of year (t) through June of year (t+1). Momentum is the average return over the past four earnings announcements. The breakpoints on book-to-market and size are based on the NYSE as reported on Ken French’s website. The benchmark portfolios include only stocks with positive book equity that are ordinary common stocks (CRSP share codes 10 or 11). It is impractical to do a 5x5x5x5 sort and thus control for overall return momentum, but we have found that switching the earnings announcement momentum control with an overall momentum control leads to similar results. 5 Turnover data for 1991 is missing in the CRSP database. Also, CRSP sometimes reports several classes of shares for a given fund, corresponding to different fee structures for the same portfolio of stocks (e.g. A, B, C, institutional, no-load). In these cases, we take the highest reported value for turnover across all classes to use as the value for turnover, and the value-weighted average of expenses across all classes as the value for the expense ratio.

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Gruber, and Blake (2003). These data are originally from Lipper and cover through 1999.

Fee structures are generally similar across the funds that use them, so we simply study an

indicator variable for whether the fund has an incentive fee in place.

Finally, we apply a few screens to obtain the most appropriate sample. Based on

keywords in the name of the fund and on reported investment objectives, we exclude

funds that cannot be predominantly characterized as actively managed U.S. equity funds,

such as index, bond, international, and precious metals funds. We exclude funds with less

than $10 million in net asset value. Finally, we exclude each fund’s first report date, as

some of our analysis requires lagged portfolio weights.

B. Summary statistics

Our final sample consists of 6.3 million fund-report date-holding observations

with associated earnings announcement returns, spread across 75,263 fund-report dates.

Table 1 shows summary statistics. The first column shows that the number of funds filing

with the SEC has increased dramatically over the sample period. Almost half of the

useable fund-report dates are in the last five years of the sample.

The next three columns show the distribution of investment objectives for these

fund-report dates. A consistent, comprehensive set of objectives is not available. CDA

classifications are available from 1980 through 1992, with a methodology change in

1990. S&P provide a broader set of objectives, but do not start until 1992. By combining

these data we obtain a fairly consistent classification for growth, growth & income, and

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income styles. The remaining funds generally fall into the balanced, sector, or total return

categories.6

The next five columns show fund holdings and trading activity. For the average

fund-report date we are able to identify and benchmark a total of 84.0 holdings. Portfolio

breadth has increased steadily over time. On average, 51.7 holdings receive an increase in

weight in the portfolio over that in the prior report, of which 20.5 are new first buys. 50.8

holdings receive a decrease in weight, on average, and 18.5 of these decrease to zero

weight. We also distinguish the performance of first buys and last sells with the view that

these are especially likely to reflect a deliberate trading decision; by contrast, generic

weight shifts may be caused by changes in overall fund size.7

The last columns summarize fund characteristics. Fund size is computed (from the

holdings data) as the total market capitalization of the reported equity holdings for which

we also have earnings announcement return data. Average size peaks at $84.1 million in

2000. Turnover is available for 71 percent of the sample, averages 95.1 percent per year

(for the sub-sample for which it is available), and increases by 37 percentage points over

the sample period. The expense ratio is available for 76 percent of the sample, averages

6 From 1980 through 1989, the CDA investment objective is available for 76 percent of the sample fund-report dates. 92 percent of the non-missing observations are categorized as growth (44 percent), maximum capital gains (21 percent), growth and income (19 percent), and income (9 percent). In 1990 and 1991, the CDA investment objective is available for 79 percent of the sample. We group the first two into growth funds. 86 percent of the non-missing observations are categorized as maximum capital gains (14 percent), long-term growth (38 percent), small capitalization growth (4 percent), growth and current income (23 percent), equity income (4 percent), and flexible income (3 percent). We group the first three categories into growth funds, and the last two into income funds. The other significant classifications are balanced and sector. From 1992 through 2002, the S&P investment objective is available for 73 percent of the sample. 76 percent of the nonmissing observations are categorized as aggressive growth (22 percent), long-term growth (32 percent), growth and income (18 percent) and income (5 percent). We group the first two categories into growth funds. The other significant classifications are balanced, sector, and total return. 7 Another natural way to define trading activity is to track changes in reported shares across report dates (adjusting for splits). Not surprisingly, the results for this measure tend to be bracketed by those for generic weight shifts and teminal/initiating trades, and so we omit them for brevity.

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1.25 percent per year (for the sub-sample for which it is available), and increases by 45

basis points over the period. The last column shows the percentage of funds that use

incentive fees. In the average year for which we have data, 2.2 percent of funds use fees.

Elton et al. (2003) report that these funds account for around 10 percent of all mutual

fund assets. As some of these characteristics appear to contain trends, we will sort funds

into quintiles within each reporting period when we examine the relationship between

characteristics and performance.

III. Results

A. Earnings announcement alphas based on holdings

Table 2 starts by summarizing the average performance of mutual fund holdings

around earnings announcements. The first column considers the raw return over the

three-day window around earnings announcement dates. We take the equal-weighted

average earnings announcement return for each fund-report date, annualize it

(multiplying by 4 quarters), average these across all fund-report dates within that year,

and, finally, average the yearly averages.8 That is, the average return of 1.08 at the

bottom of the first column is given by:

Return = ∑ ∑ ∑ ∑−⋅

2002

1980

1

1 ,11

2314

i j tijKN ri

, (1)

where i indexes mutual funds from 1 to N, j indexes the holdings of mutual fund i from 1

to Ki, and t measures days around the earnings announcement of stock ij.

This treats each annual average as a single data point in computing an overall

average and standard error at the bottom of the table. This approach, which is in the spirit 8 Because the sample starts in the second quarter of 1980 and ends in the third quarter of 2002, the average return for 1980 is for the last three quarters while the average return for 2002 is the first three quarters.

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of Fama and MacBeth (1973), gives equal weight to each time period. Taking simple

averages across the pooled data, which gives more weight to the last five years of the

sample, leads to similar inferences. The standard deviation of the annual averages is 1.34.

Combining this with the average return of 1.08 and the sample size of 23 gives a t-

statistic of 3.9.

The second and third columns adjust the raw returns. The second column reports

market-adjusted returns (MAR), where we subtract the CRSP value-weighted market

return over the earnings announcement window. The average MAR of 0.52 is:

MAR = ( )∑ ∑ ∑ ∑−−⋅

2002

1980

1

1 ,,11

2314

i j tmtijKN rri

. (2)

The t-statistic here is 3.5.

More interestingly, the third column shows a benchmark-adjusted return (BAR),

where each holding is matched to one of 125 benchmark portfolios by quintiles of size,

book-to-market, and earnings announcement return momentum. That is, the benchmark

portfolios contain the value-weighted, matched-firm average earnings announcement

return in that calendar quarter. The average BAR of 0.01 is then:

BAR = ( )∑ ∑ ∑ ∑∑∑ −=−=−⋅

2002

1980

1

1 ,1

1 ,11

2314

i j s sll lt tijKNl li

rwr , (3)

where l indexes the characteristics-matched firms within the quarter where t is equal to

zero, wl is the market value weight of stock l in the characteristics-matched portfolio, and

sl measures days around the earnings announcement of stock l within the matched

quarter. Note that in Eq. (3) the earnings announcement return and the benchmark do not

overlap exactly.

BAR controls for the known predictive power of firm characteristics and prior

earnings announcement returns for future announcement returns. For instance, La Porta et

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al. (1997) find that small and high book-to-market firms tend to have higher earnings

announcement returns than large and low book-to-market firms. Bernard and Thomas

(1989) find that earnings announcement returns are positively autocorrelated. BAR

controls for these effects. Further, in allowing the benchmark return to vary from quarter

to quarter, BAR also controls for a “good earnings quarter for small value stocks,” for

example, and thus more precisely picks up individual stock-selection skill.

Of course, it may also be a valuable skill for a manager to be able to predict

abnormal returns at the style level, as well as to recognize and exploit the positive

autocorrelation in abnormal announcement returns or characteristics reliably associated

with such abnormal returns. Because the BAR will not pick up these sources of skill, it is

likely to be a conservative measure, and to understate the abilities of mutual funds to

outperform. Choosing a conservative measure is in keeping with the basic goal of the

earnings announcement alpha approach, outlined in the introduction, which is to establish

a sturdy, and statistically significant, lower bound on abnormal performance. Naturally it

is also of interest if even this lower bound turns out to be large.

Table 2 shows that mutual funds earn, on an equal-weighted average basis, 1.08

percent per year from the twelve trading days surrounding their holdings’ earnings

announcements. This exceeds the corresponding market return by 52 basis points, and so

is an outsize average return compared to non-announcement days. The raw annualized

announcement return earned by the average fund manager is not significantly larger than

that earned on a portfolio of firms with matching characteristics and prior earnings

announcements: the average BAR is an insignificant 6 basis points. The second set of

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columns show that similar conclusions obtain when holdings are value-weighted in each

fund-report date.

To the extent that the BAR accurately measures the unexpected release of

information, then the average mutual fund, as measured by its holdings, does not appear

to possess stock-picking ability. This would be consistent with the message of Jensen

(1968), Carhart (1997), and many studies in between. Of course, the conclusion that no

mutual fund manager has skill is clearly premature. A subset of managers may have skill,

even if the average one does not. Alternatively, funds may hold many stocks for which

they once had good information but now retain because of transaction costs or a capital

gains tax overhang, an effect which would reduce the power of our tests. We turn to these

possibilities next.

As an aside, the high average MAR—indicating that while funds’ holdings earn

above-market returns around earnings announcements, so does the average stock—raises

a question of the extent to which even an event-study approach is able to fully resolve the

joint-hypothesis problem. There are two interpretations. At one extreme, the high average

MAR might be a general inefficiency, an irrational discount on earnings announcers. Put

another way, returns around earnings announcements are in fact idiosyncratic in this

interpretation, but there is a high return nonetheless. In this case, the BAR separates

novel stock-picking skill from known mispricings related to size, book-to-market, and

past momentum. At the other extreme, the high MAR might reflect the realization of a

rational risk premium. Namely, the earnings announcement return is systematic and

echoed in aggregate returns across a class of stocks or the market as a whole. Then, BAR

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is best seen as a control for risk, and to the extent that it is imperfect, at least some joint-

hypothesis problem inevitably remains.

We lean toward the first interpretation. The results of Ball and Kothari (1991) and

Bernard and Thomas (1989) suggest that the returns around earnings announcements are

largely idiosyncratic.9 Moreover, Fama (1991) notes that the use of earnings

announcement returns, if still somewhat imperfect, is probably the closest one can come

to solving the joint hypothesis problem. We return to these issues in our analysis of fund

trades.

B. Fund characteristics and alphas based on holdings

We next look for regularities in the distribution of earnings announcement alphas.

Under the null hypothesis of no stock-picking skill, no such patterns will be apparent. We

first look at performance persistence, which has been studied in long-horizon returns by

Hendricks, Patel, and Zeckhauser (1993), Brown and Goetzmann (1995), and subsequent

authors. Do the same funds that had high earnings announcement alphas in the past

continue to have them in the future?10

Table 3 shows the results of tests for persistence. Following Hendricks et al.

(1993) and Carhart (1997), we sort stocks each year from 1983 to 2002 into quintiles

based on the average announcement return, or the average BAR alpha, that they earned

over their previous eight announcements. We then compare the subsequent annualized 9 In particular, Ball and Kothari show that betas increase only slightly around earnings announcements, while the positive autocorrelation in returns shown by Bernard and Thomas suggests that a risk premium is unlikely to be a complete explanation for announcement effects. 10 Our tests operate at the level of funds rather than managers. Because it is possible that a manager has changed over the interval that we measure persistence, our tests may understate the true level of persistence in manager returns. Studies that control for changes in fund management include Baks (2004), and Ding and Wermers (2004).

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announcement returns and BAR alphas of funds in the top quintile of prior performance

to those in the bottom quintile.

The first four columns show the mean subsequent equal-weighted return and BAR

alpha, where the sorting variable is, respectively, previous equal-weighted return and

previous BAR alpha. Also shown are the corresponding t-statistics. There is significant

persistence in earnings announcement alphas both in raw and benchmark-adjusted

returns. When sorted by prior equal-weighted BAR, the subsequent equal-weighted BAR

rises monotonically. The difference between the top and bottom quintiles is a significant

43 basis points per year. The fact that persistence is present in BAR, i.e., even after

adjustments are made for size, book-to-market, and announcement return momentum,

indicates that performance persistence cannot be explained by persistence in

characteristics-adjusted announcement returns alone.11 Value-weighted results display a

similar but weaker pattern, suggesting it may be easier to pick future earnings winners

among smaller stocks.

Table 4 looks at how performance is correlated with fund characteristics. Panel A

considers investment objective, including growth, growth and income, and income styles.

A clear pattern emerges. Growth funds earn higher earnings announcement returns than

growth and income funds, which in turn earn higher returns than income funds. The same

pattern is as strong, or stronger, in BAR alphas. Indeed, the BAR on the portfolio of

growth funds is positive, while the BAR on income and growth and income funds is

negative. One Wald test (W1 in the table) strongly rejects that the average return for each

category is equal to zero, and a second (W2) strongly rejects that fund categories are

11 This is where it is crucial to control for prior earnings announcements. In the absence of such a control, the Bernard and Thomas (1989) effect could lead to a spurious persistence.

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equal to each other. Finally, comparing each style to the equal-weighted average of the

other two reveals that income funds perform significantly worse than growth and growth

and income categories. Similarly, growth funds perform significantly better. These results

are consistent with long-horizon studies by Grinblatt and Titman (1989, 1993) and Daniel

et al. (1997), who also find the most evidence of stock-selection ability among growth

and aggressive growth funds. Here, we tie these earlier results closely to information-

based trading.

Panel B examines returns by fund size quintiles. There is some evidence that

performance around earnings announcements increases with fund size; specifically, the

smallest quintile does worse than any of the larger quintiles. In unreported results, we

find that the significance of this pattern is higher if one uses the number of holdings to

measure fund size. Interestingly, the pattern here is opposite to the results of the long-

horizon study by Chen, Hong, Huang, and Kubik (2003).

So far, we have seen that funds with high earnings announcement alphas can be

identified from past performance, style, and, to some extent, size. One possibility is that

differential performance is associated with, or perhaps facilitated by, higher expenses.

Panel C shows that this is not the case. Expenses bear little relation to performance. In

contrast, there is strong evidence that high earnings announcement alphas are associated

with high turnover. Panel D shows that across all four performance measures, funds in

the highest turnover quintile have significantly higher performance.

Finally, Panel E considers the effect of incentive fees. By all measures of earnings

announcement alpha, funds with incentive fees earn higher returns around earnings

announcements. The difference is statistically significant for three measures. This

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reinforces the long-horizon results of Elton, Gruber, and Blake (2003), and again ties

these earlier results more closely to information-based trading.

C. Earnings announcement alphas based on trades

We now make more powerful use of these data by examining fund trades.

Because trading involves transaction costs and perhaps the realization of capital gains,

trading may be a stronger signal than simply continuing to hold. Table 5 repeats the

analysis from Table 2 but computes announcement returns only for holdings whose

portfolio weight changed between the current and the previous report dates. The first

three pairs of columns show equal-weighted raw and benchmark-adjusted returns for

holdings whose weight increased or decreased. The second three pairs of columns focus

only on first buys, i.e., when a fund moves from zero to a positive holding of the stock,

and last sells, i.e., when a fund liquidated the holding.

Table 5 contains the main results of the paper. Stocks in which funds have

increased their weight earn a statistically significant 20 annualized basis points more

around the next earnings announcement than stocks of similar characteristics and prior

announcement returns. Moreover, stocks in which funds have decreased their weight earn

a significant 21 annual basis points less than matched stocks. Initiating buys and terminal

sells reflect even stronger information: first buys earn 34 basis points more than matching

stocks, while last sells earn 29 basis points less. Thus, the stocks that funds buy perform

considerably better at subsequent announcements than those they sell.

This analysis also helps to address any residual joint-hypothesis problem that

affects our BAR alphas based on holdings. The raw returns are fairly large for both buys

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and sells, suggesting there is either a generic mispricing surrounding the revelation of

idiosyncratic earnings announcement news or a rational risk premium. However, it is

difficult to see why funds would systematically buy (sell) stocks with higher (lower)

levels of risk. Rather, Table 5 provides a fairly clean demonstration that the average

mutual fund displays some stock-picking skill in both its buys and its sells.

Table 5 also shows that the difference in total announcement returns between

buys and sells is very close to the difference in BARs. The difference in total returns is

slightly higher, indicating a general tendency to rebalance toward the characteristics

associated with better subsequent announcement returns. However, the fact that the two

measures are so close indicates that the bulk of the total difference between buys and

sells is due to picking winners and losers within characteristic groupings, rather than

choosing the winning characteristics.

Overall, these results offer considerably more convincing evidence of skill, in

suggesting that even the average fund manager trades as if he has superior information

about the earnings prospects of firms. While a direct comparison to Jensen (1968) is not

appropriate, the gist of our results contrasts with his oft-cited message that the average

fund underperforms. More broadly, our results from trades complement the findings of

Chen, Jegadeesh, and Wermers (2000). Chen et al. document a gap between the long-

horizon returns between the stocks that mutual funds buy and those they sell. We show

that at least a portion of this gap can be closely tied to information-based trading.

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D. Fund characteristics and alphas based on trades

The last analysis combines the power of following trades and of sorting on fund

characteristics. As in Section B, we start with persistence. Table 6 tests for persistence in

each of the six trade-based BAR alpha measures and the six raw return measures. For

each measure, we sort funds into quintiles based on their previous performance over the

past two years, and then tabulate their subsequent performance.

We find evidence of performance persistence in alphas based on trades, in

particular alphas based on weight increases, weight decreases, and the difference. The

gap between the BAR for the highest and lowest weight increase quintiles is a significant

37 basis points per annum, and the gap for weight decreases is an even larger 60 basis

points. (Recall that sorting across quintiles has the opposite interpretation for weight

increases and decreases. For weight increases, high BAR indicate forecasting skill, while

for decreases, low BAR indicate skill.) There is little evidence of persistence in relative

performance of first buys, last sells, and first buys minus last sells. The likely explanation

is that classifications based on the performance of past first buys or last sells are less

precise, there being far fewer such trades than generic buys or sells. This does not affect

the means in Table 5, but does reduce the ability to classify a fund here based on past

performance.

Finally, Table 7 examines the relation between fund characteristics and alphas

based on trades. Panel A shows that growth funds again appears to outperform income

funds based on these measures. The Wald tests again reject the hypothesis of equality in

most cases. The remaining panels usually point in the same direction as the earlier results

based on holdings, but tend to be weaker. Larger funds tend to outperform smaller funds,

19

expense ratios do not matter at all, and turnover and incentive fees are weakly positively

correlated with performance. Given the stronger results of Table 5, the main takeaway is

that various categories of funds buy subsequent earnings winners and sell subsequent

earnings losers, but there are also differences in this measure of performance across style

and other characteristics.

IV. Summary

We develop a methodology to measure the stock-picking skill by fund managers

based on their holdings and trades prior to earnings announcements. Our approach offers

several advantages. It uses the segment of returns data which is most informative about a

fund manager’s view on stock fundamentals. Further, it helps to distinguish evidence on

abnormal performance from evidence on model misspecification, thereby largely

avoiding the joint-hypothesis problem common to long-horizon studies of fund manager

skill. We suggest that the “earnings announcement alpha” methodology offers a useful

complement to standard, long-horizon measures of fund performance.

Using this methodology, we uncover new evidence that fund managers have at

least some stock-picking skill. In particular, the future earnings announcement returns on

stocks that funds buy are, on average, considerably higher than the future announcement

returns on stocks that they sell. Very little of this difference reflects movement toward

categories of stocks (size, book-to-market, and prior announcement returns) that are

about to earn higher announcement returns. Instead, most of the effect comes from

picking stocks within these categories: The stocks that funds buy perform significantly

better at future earnings announcements than stocks with similar characteristics, and vice-

20

versa with stocks that funds sell. Along the way, we also note several cross-sectional

patterns, such as the stronger performance of growth funds and funds with incentive fees,

which were suggested in previous long-horizon studies but had never been closely tied to

information-based trading.

21

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Table 1. Summary statistics, 1980Q2 through 2002Q3. The sample is the intersection of the Spectrum Mutual Fund holdings database, Compustat, and CRSP. To be included in the sample, a mutual fund holding must have matched earnings announcement date and book value from CRSP, and a valid return, market value of equity (price times shares outstanding), past momentum (return from months t-12 through t-2), and three-day return in the earnings announcement window from CRSP. We compute terminal holdings for stocks that exit the portfolio. Where possible, we include the investment objective from the CRSP mutual fund database as determined by CDA Weisenberger or S&P. The investment objective growth includes codes G, MCG, and LTG from CDA and LG, and AG from S&P. The investment objective growth and income includes G-I and GCI from CDA and GI and IN from S&P. The investment objective income includes I, IEQ, and IFL from CDA and IN from S&P. We classify each holding as a weight increase or weight decrease. We also record those weight increases that are first buy (from zero to positive weight), and those weight decreases that are last sells (from positive weight to zero). We measure fund size as the total market value (price times shares outstanding) of its reported equity holdings; fund turnover and fund expense ratio from the CRSP mutual fund database; and incentive fees (whether or not the fund has such a structure) from Blake, Elton, and Gruber (2003) and Lipper. Turnover is missing in CRSP in 1991 and incentive fees are not available after 1999.

Fund-Report Date Observations Average Fund Activity Fund Characteristics

Year All Growth Growth&

Income Income Holdings Weight

Increases Weight

Decreases First Buys

Last Sells

Size ($M)

Turnover (%)

Expenses (%)

Inc. Fees (% Yes)

1980 810 382 107 25 49.1 27.3 28.3 6.9 6.5 14.2 75.3 0.94 0.6 1981 1,088 494 137 27 49.0 29.4 26.8 6.5 7.2 13.6 68.5 0.92 1.5 1982 903 430 122 32 49.6 29.5 29.3 9.2 9.2 14.1 74.0 0.95 2.5 1983 1,085 525 142 56 57.8 33.0 34.8 11.4 10.0 19.7 77.3 0.94 2.5 1984 1,218 579 170 71 59.0 35.0 34.5 10.5 10.5 17.8 72.9 0.96 2.4 1985 1,362 660 196 94 58.5 34.7 34.5 11.4 10.6 20.5 80.8 0.97 2.6 1986 1,530 756 224 149 60.4 35.5 36.5 12.3 11.6 24.8 78.6 0.99 2.7 1987 1,742 872 266 173 63.9 37.6 39.0 13.7 12.7 30.2 96.0 1.06 3.1 1988 1,843 931 298 168 63.8 38.6 35.8 11.3 10.6 25.1 81.5 1.18 3.2 1989 1,879 971 272 158 64.3 38.2 37.6 12.6 11.4 27.4 77.8 1.20 2.3 1990 2,012 888 370 129 65.0 37.8 39.1 12.0 12.0 26.3 88.8 1.24 2.4 1991 2,242 984 401 121 68.6 39.4 41.4 14.3 12.3 30.8 n.a. 1.23 2.2 1992 2,519 1,054 506 171 75.1 43.7 45.4 15.4 14.0 37.5 80.1 1.25 2.6 1993 2,747 1,159 466 143 84.1 49.3 51.3 19.5 16.5 44.3 80.1 1.24 2.6 1994 3,352 1,277 520 146 85.2 51.2 53.5 21.1 19.4 39.4 81.8 1.24 2.3 1995 3,552 1,432 562 149 89.3 54.6 56.5 24.6 21.7 49.3 88.4 1.25 2.3 1996 4,212 1,690 623 168 90.9 56.7 57.7 27.0 23.6 55.9 91.4 1.28 2.2 1997 4,872 2,126 678 191 90.9 58.1 56.0 25.9 23.3 65.6 91.9 1.26 2.1 1998 5,283 2,385 770 217 90.0 56.0 56.8 23.8 22.8 79.8 89.7 1.28 2.1 1999 6,352 2,722 803 232 88.7 53.7 55.4 23.7 20.4 84.0 88.1 1.30 1.4 2000 8,340 3,164 923 224 95.3 60.1 57.2 24.8 22.0 84.1 116.4 1.30 n.a. 2001 9,018 3,092 881 170 95.0 60.1 55.2 23.5 20.4 60.8 118.1 1.34 n.a. 2002 7,302 2,640 700 157 96.6 60.4 56.1 20.8 19.8 57.7 112.0 1.39 n.a.

All 75,263 31,213 10,137 3,171 84.0 51.7 50.8 20.5 18.5 54.8 95.1 1.25 2.2

Table 2. Annualized announcement effects. For each periodic mutual fund holdings report, we compute the average subsequent quarterly earnings announcement return: raw, market-adjusted, and benchmark-adjusted; and equal- and value-weighted across all holdings by fund. The characteristics benchmark return is the corresponding 5x5x5 size, book-to-market, and momentum average earnings announcement return in the matched quarter. Momentum here is defined as the return in the past 4 earnings announcements. We annualize these returns (multiplying by four) and average across all funds within a year. Returns are Winsorized at the top and bottom one percent.

EW Earnings Announcement Alpha VW Earnings Announcement Alpha

Year Return MAR BAR Return MAR BAR 1980 -0.09 -0.49 -0.15 -0.09 -0.44 -0.03 1981 0.78 0.61 0.15 1.17 1.02 0.52 1982 1.38 0.38 0.54 1.39 0.47 0.54 1983 -0.85 0.00 0.05 -0.96 -0.09 0.01 1984 1.49 -0.06 0.40 1.65 0.05 0.41 1985 1.09 -0.42 -0.07 1.39 -0.14 0.08 1986 1.93 0.46 0.49 2.26 0.75 0.68 1987 -2.19 0.19 -0.62 -2.30 0.35 -0.69 1988 0.17 -0.01 -0.40 0.32 0.14 -0.31 1989 0.05 -0.45 0.21 0.18 -0.33 0.25 1990 1.86 0.71 0.22 2.00 0.76 0.23 1991 1.37 0.80 -0.10 1.24 0.60 -0.17 1992 1.80 0.65 -0.04 1.76 0.58 -0.09 1993 0.80 0.84 0.00 0.80 0.82 -0.11 1994 0.92 0.30 -0.17 1.01 0.39 -0.23 1995 2.46 0.92 -0.07 2.53 0.98 -0.07 1996 2.53 1.67 0.21 2.72 1.87 0.23 1997 3.51 1.32 0.13 3.62 1.40 0.08 1998 1.43 0.42 0.12 1.54 0.44 0.01 1999 3.04 2.67 0.56 3.29 2.95 0.81 2000 -1.26 0.12 0.73 -1.31 0.20 0.80 2001 1.58 0.48 -0.55 1.53 0.50 -0.65 2002 1.08 0.90 -0.33 1.41 1.18 -0.14

Avg 1.08 0.52 0.06 1.18 0.63 0.09 SD 1.34 0.71 0.35 1.41 0.75 0.40 [t] [3.9] [3.5] [0.8] [4.0] [4.0] [1.1]

Table 3. Annualized announcement effects: Persistence. For each periodic mutual fund holdings report, we compute the average subsequent quarterly earnings announcement return: raw and benchmark-adjusted; and equal- and value-weighted across all holdings by fund. The characteristics benchmark return is the corresponding 5x5x5 size, book-to-market, and momentum average earnings announcement return in the matched quarter. Momentum here is defined as the return in the past 4 earnings announcements. We annualize these returns (multiplying by four) and average across all funds within each past performance quintile for each report date (quintiles go from lowest past performance to highest). Past performance is defined based on the previous eight holdings reports (for the corresponding definition of performance). Returns are Winsorized at the top and bottom one percent.

Past Return EW Earnings Announcement Alpha VW Earnings Announcement Alpha

Quintile Return [t] BAR [t] Return [t] BAR [t] 1 1.13 [ 4.0] -0.17 [-1.3] 1.24 [ 4.4] -0.09 [-0.8] 2 1.20 [ 4.1] -0.08 [-0.7] 1.33 [ 3.9] -0.20 [-1.5] 3 1.43 [ 4.1] -0.06 [-0.6] 1.30 [ 3.9] -0.05 [-0.5] 4 1.37 [ 4.3] 0.01 [ 0.1] 1.45 [ 4.5] 0.07 [ 0.6] 5 1.47 [ 4.1] 0.25 [ 1.7] 1.51 [ 3.8] 0.10 [ 0.6]

5-1 0.34 [ 2.9] 0.43 [ 3.4] 0.27 [ 1.5] 0.19 [ 1.2]

Table 4. Annualized announcement effects: Fund characteristics. For each periodic mutual fund holdings report, we compute the average subsequent quarterly earnings announcement return: raw and benchmark-adjusted; and equal- and value-weighted across all holdings by fund. The characteristics benchmark return is the corresponding 5x5x5 size, book-to-market, and momentum average earnings announcement return in the matched quarter. Momentum here is defined as the return in the past 4 earnings announcements. We annualize these returns (multiplying by four) and average across all funds by investment objective (style), total market value of reported holdings (fund size), expense ratio, turnover, and incentive fee structure for each report date. For size, expense ratio, and turnover, quintiles go from lowest to highest. Returns are Winsorized at the top and bottom one percent. For the style categories we perform Wald tests of the joint hypothesis that all three groups have returns equal to zero (W1) or a constant (W2).

EW Earnings Announcement Alpha VW Earnings Announcement Alpha

Return [t] BAR [t] Return [t] BAR [t] Style Panel A. Style

G 1.32 [ 4.1] 0.13 [ 1.0] 1.42 [ 4.3] 0.13 [ 1.0] G&I 1.23 [ 5.1] -0.08 [-0.9] 1.26 [ 4.6] -0.11 [-1.2]

I 0.86 [ 3.7] -0.45 [-2.9] 0.92 [ 3.9] -0.44 [-2.4] W1 30.50 24.85 21.31 16.85 [p] 0.00 0.00 0.00 0.00

W2 23.16 23.16 16.84 16.84 [p] 0.00 0.00 0.04 0.00

G,<G&I,I> [ 1.6] [ 3.7] [ 2.0] [ 4.0] G&I,<G,I> [ 1.4] [ 0.9] [ 0.8] [ 0.5] I,<G,G&I> [-3.0] [-4.7] [-2.5] [-3.3]

Quintile Panel B. Size 1 1.15 [ 3.9] -0.05 [-0.5] 1.25 [ 4.0] 0.01 [ 0.0] 2 1.27 [ 4.6] 0.05 [ 0.6] 1.31 [ 4.4] 0.03 [ 0.4] 3 1.23 [ 4.2] 0.01 [ 0.1] 1.35 [ 4.5] 0.06 [ 0.6] 4 1.24 [ 4.1] 0.03 [ 0.2] 1.32 [ 4.2] 0.01 [ 0.1] 5 1.26 [ 4.5] 0.02 [ 0.2] 1.38 [ 4.7] 0.04 [ 0.4]

5-1 0.11 [ 1.7] 0.07 [ 1.4] 0.13 [ 2.2] 0.04 [ 0.7] Quintile Panel C. Expense Ratio

1 1.26 [ 4.8] 0.00 [ 0.0] 1.34 [ 4.7] -0.01 [-0.1] 2 1.25 [ 4.5] -0.01 [-0.1] 1.32 [ 4.5] -0.02 [-0.1] 3 1.20 [ 4.0] -0.03 [-0.2] 1.27 [ 4.1] -0.03 [-0.2] 4 1.17 [ 3.8] -0.03 [-0.2] 1.26 [ 4.0] -0.03 [-0.2] 5 1.28 [ 4.2] 0.11 [ 0.9] 1.36 [ 4.4] 0.09 [ 0.8]

5-1 0.02 [ 0.2] 0.10 [ 1.0] 0.03 [ 0.3] 0.10 [ 1.1] Quintile Panel D. Turnover

1 1.16 [ 4.5] -0.07 [-0.8] 1.28 [ 4.5] -0.03 [-0.3] 2 1.10 [ 4.1] -0.11 [-0.7] 1.19 [ 4.2] -0.10 [-0.7] 3 1.17 [ 3.7] -0.03 [-0.2] 1.21 [ 3.7] -0.07 [-0.6] 4 1.26 [ 3.8] 0.04 [ 0.3] 1.34 [ 4.1] 0.04 [ 0.3] 5 1.50 [ 4.6] 0.27 [ 1.9] 1.59 [ 4.7] 0.26 [ 1.8]

5-1 0.34 [ 2.0] 0.34 [ 2.9] 0.32 [ 2.0] 0.29 [ 2.7] Fees Panel E. Incentive Fees Yes 1.49 [ 4.5] 0.27 [ 2.3] 1.64 [ 4.8] 0.23 [ 1.4] No 1.27 [ 4.2] 0.08 [ 1.1] 1.37 [ 4.3] 0.10 [ 1.2]

Yes-No 0.22 [ 2.1] 0.19 [ 1.8] 0.27 [ 1.7] 0.13 [ 0.8]

Table 5. Annualized announcement effects: Mutual fund trades. For each periodic mutual fund holdings report, we compute the average subsequent quarterly earnings announcement returns: raw and benchmark-adjusted; and equal-weighted across weight increases, weight decreases, long weight increases and short weight decreases, first buys, last sells, and long first buys and short last sells by fund. The characteristics benchmark return is the corresponding 5x5x5 size, book-to-market, and momentum average earnings announcement return in the matched quarter. Momentum here is defined as the return in the past 4 earnings announcements. We annualize these returns (multiplying by four) and average across all funds within a year. Returns are Winsorized at the top and bottom one percent.

Weight Increases Weight Decreases Increases-Decreases First Buys Last Sells First Buys-Last Sells

Year Return BAR Return BAR Return BAR Return BAR Return BAR Return BAR 1980 -0.35 -0.33 -0.09 -0.18 -0.26 -0.15 -0.57 -0.56 -0.66 -0.69 0.09 0.14 1981 0.95 0.32 0.52 -0.06 0.43 0.38 0.61 -0.03 -0.02 -0.43 0.63 0.40 1982 1.87 0.91 0.40 -0.24 1.47 1.15 2.78 1.84 0.44 -0.34 2.34 2.19 1983 -0.71 0.07 -1.03 0.03 0.32 0.04 -0.41 0.39 -1.37 -0.44 0.96 0.83 1984 1.45 0.44 1.39 0.30 0.05 0.14 1.14 0.26 0.84 0.10 0.30 0.16 1985 1.33 0.13 0.83 -0.29 0.49 0.42 1.24 0.01 0.98 -0.21 0.26 0.22 1986 2.41 0.88 1.30 0.00 1.11 0.88 2.10 0.78 1.46 0.22 0.64 0.56 1987 -2.22 -0.64 -2.00 -0.52 -0.22 -0.12 -2.65 -0.81 -1.68 -0.38 -0.97 -0.43 1988 0.44 -0.15 -0.26 -0.82 0.70 0.67 1.00 0.35 -0.17 -0.72 1.17 1.06 1989 0.50 0.60 -0.80 -0.47 1.30 1.07 0.36 0.55 -1.14 -0.64 1.50 1.19 1990 2.11 0.38 1.24 -0.20 0.87 0.58 2.04 0.51 0.78 -0.54 1.26 1.05 1991 1.66 0.22 1.12 -0.41 0.54 0.63 1.65 0.23 1.46 -0.15 0.20 0.38 1992 1.75 -0.05 1.69 -0.09 0.06 0.04 2.40 0.69 1.21 -0.55 1.19 1.24 1993 0.77 0.02 0.84 -0.07 -0.07 0.09 0.79 0.14 1.01 0.01 -0.22 0.13 1994 1.01 -0.08 0.66 -0.43 0.34 0.35 1.11 0.23 0.55 -0.57 0.56 0.79 1995 2.49 -0.03 2.35 -0.22 0.14 0.19 3.01 0.53 2.34 -0.20 0.67 0.73 1996 2.58 0.26 2.31 0.08 0.27 0.18 2.16 0.10 2.26 0.16 -0.10 -0.05 1997 3.58 0.23 3.24 -0.07 0.34 0.30 3.41 0.44 3.12 -0.09 0.29 0.53 1998 1.47 0.11 1.30 0.23 0.17 -0.12 1.77 0.45 1.49 0.63 0.28 -0.18 1999 3.26 0.77 2.26 -0.19 1.00 0.96 3.58 1.09 1.48 -1.05 2.10 2.13 2000 -0.87 1.10 -2.08 -0.13 1.21 1.23 -1.47 0.99 -2.14 -0.45 0.67 1.44 2001 1.43 -0.54 1.69 -0.59 -0.26 0.05 1.91 -0.13 1.64 -0.52 0.28 0.38 2002 1.40 -0.09 0.67 -0.53 0.73 0.45 0.75 -0.27 1.47 0.22 -0.71 -0.49

Avg 1.23 0.20 0.76 -0.21 0.47 0.41 1.25 0.34 0.67 -0.29 0.58 0.63 SD 1.35 0.45 1.34 0.27 0.51 0.42 1.51 0.55 1.35 0.38 0.79 0.71 [t] [4.4] [2.1] [2.7] [-3.8] [4.4] [4.6] [4.0] [2.9] [2.4] [-3.6] [3.5] [4.2]

Table 6. Annualized announcement effects: Mutual fund trades and persistence. For each periodic mutual fund holdings report, we compute the average subsequent quarterly earnings announcement returns: raw and benchmark-adjusted; and equal-weighted across weight increases, weight decreases, long weight increases and short weight decreases, first buys, last sells, and long first buys and short last sells by fund. The characteristics benchmark return is the corresponding 5x5x5 size, book-to-market, and momentum average earnings announcement return in the matched quarter. Momentum here is defined as the return in the past 4 earnings announcements. We annualize these returns (multiplying by four) and average across all funds within each past performance quintile for each report date (quintiles go from lowest past performance to highest). Past performance is defined based on the previous eight holdings reports (for the corresponding definition of performance). Returns are Winsorized at the top and bottom one percent.

Past Return Weight Increases Weight Decreases Increases-Decreases First Buys Last Sells First Buys-Last Sells

Quintile Return BAR Return BAR Return BAR Return BAR Return BAR Return BAR 1 1.27 -0.04 0.70 -0.61 0.08 0.10 1.47 0.25 0.90 -0.37 0.69 0.63 2 1.37 -0.02 1.09 -0.21 0.37 0.17 1.45 0.30 0.82 -0.45 0.61 0.57 3 1.38 0.05 1.26 -0.05 0.24 0.27 1.47 0.05 0.91 -0.24 0.35 0.58 4 1.54 0.17 1.21 -0.11 0.43 0.45 1.55 0.40 0.88 -0.28 0.79 0.61 5 1.48 0.33 1.27 -0.01 0.56 0.51 1.34 0.36 0.79 -0.46 0.48 0.63

5-1 0.21 0.37 0.57 0.60 0.48 0.40 -0.12 0.10 -0.11 -0.09 -0.21 0.00 [t] [ 1.6] [ 2.4] [ 2.1] [ 2.1] [ 1.9] [ 2.6] [-0.4] [ 0.4] [-0.3] [-0.3] [-0.5] [ 0.0]

Table 7. Annualized announcement effects: Mutual fund trades and fund characteristics. For each periodic mutual fund holdings report, we compute the average subsequent quarterly earnings announcement returns: raw and benchmark-adjusted; and equal-weighted across weight increases, weight decreases, long weight increases and short weight decreases, first buys, last sells, and long first buys and short last sells by fund. The characteristics benchmark return is the corresponding 5x5x5 size, book-to-market, and momentum average earnings announcement return in the matched quarter. Momentum here is defined as the return in the past 4 earnings announcements. We annualize these returns (multiplying by four) and average across all funds by investment objective (style), total market value of reported holdings (fund size), expense ratio, turnover, and incentive fee structure for each report date. For fund size, expense ratio, and turnover, quintiles go from lowest to highest. Returns are Winsorized at the top and bottom one percent. For the style categories we perform Wald tests of the joint hypothesis that all three groups have returns equal to zero (W1) or a constant (W2).

Weight Increases

Weight Decreases

Increases-Decreases First Buys Last Sells

First Buys- Last Sells

Ret BAR Ret BAR Ret BAR Ret BAR Ret BAR Ret BAR Style Panel A. Style

G 1.48 0.29 0.89 -0.27 0.59 0.56 1.48 0.40 0.70 -0.41 0.78 0.81 G&I 1.37 0.03 1.06 -0.21 0.31 0.24 1.60 0.38 0.86 -0.40 0.73 0.79

I 0.97 -0.35 0.77 -0.47 0.20 0.12 1.20 -0.04 0.94 -0.32 0.27 0.28 W1 29.31 22.11 18.82 10.73 20.88 28.07 38.21 12.85 12.35 10.37 21.99 29.00 [p] 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01 0.02 0.00 0.00

W2 19.31 19.31 3.47 3.47 10.72 10.72 4.25 4.25 0.08 0.08 2.50 2.50 [p] 0.01 0.00 0.21 0.18 0.04 0.00 0.19 0.12 0.76 0.96 0.35 0.29

G,<G&I,I> [ 1.7] [ 3.7] [-0.1] [ 0.4] [ 2.5] [ 3.0] [ 0.3] [ 1.1] [-0.7] [-0.2] [ 1.3] [ 1.4] G&I,<G,I> [ 1.0] [ 0.4] [ 1.5] [ 1.3] [-0.7] [-0.8] [ 1.4] [ 1.1] [ 0.2] [-0.2] [ 0.8] [ 0.9] I,<G,G&I> [-2.9] [-3.7] [-1.2] [-1.6] [-1.5] [-1.5] [-1.4] [-2.1] [ 0.4] [ 0.3] [-1.4] [-1.5]

Quintile Panel B. Size 1 1.29 0.08 0.87 -0.31 0.42 0.38 1.17 0.03 0.79 -0.38 0.37 0.42 2 1.38 0.16 0.95 -0.22 0.44 0.38 1.46 0.40 0.92 -0.26 0.54 0.65 3 1.41 0.17 0.90 -0.26 0.51 0.43 1.55 0.42 0.92 -0.24 0.63 0.66 4 1.39 0.18 0.95 -0.25 0.45 0.44 1.57 0.46 0.84 -0.28 0.73 0.73 5 1.41 0.16 0.91 -0.28 0.50 0.44 1.44 0.33 0.56 -0.62 0.88 0.95

5-1 [ 1.3] [ 1.0] [ 0.4] [ 0.3] [ 0.6] [ 0.6] [ 1.3] [ 1.4] [-1.2] [-1.3] [ 1.9] [ 2.0] Quintile Panel C. Expense Ratio

1 1.40 0.10 0.97 -0.23 0.43 0.33 1.52 0.37 0.71 -0.52 0.80 0.88 2 1.39 0.13 0.99 -0.26 0.40 0.39 1.39 0.28 0.86 -0.34 0.53 0.62 3 1.37 0.13 0.91 -0.29 0.45 0.42 1.50 0.40 1.00 -0.18 0.51 0.58 4 1.44 0.24 0.70 -0.47 0.74 0.71 1.49 0.40 0.55 -0.53 0.94 0.94 5 1.36 0.18 0.86 -0.29 0.49 0.47 1.44 0.35 0.63 -0.46 0.81 0.81

5-1 [-0.3] [ 0.7] [-0.9] [-0.5] [ 0.4] [ 1.2] [-0.4] [-0.1] [-0.4] [ 0.3] [ 0.0] [-0.3] Quintile Panel D. Turnover

1 1.30 0.07 0.88 -0.33 0.42 0.39 1.21 0.14 0.66 -0.50 0.55 0.64 2 1.26 0.02 0.84 -0.32 0.41 0.34 1.20 0.18 0.63 -0.54 0.57 0.72 3 1.33 0.14 0.97 -0.19 0.37 0.33 1.47 0.37 0.99 -0.11 0.48 0.48 4 1.45 0.20 0.81 -0.34 0.64 0.54 1.52 0.36 0.64 -0.45 0.88 0.81 5 1.60 0.37 0.94 -0.26 0.65 0.63 1.67 0.50 0.80 -0.38 0.87 0.88

5-1 [ 1.7] [ 2.3] [ 0.4] [ 0.6] [ 1.3] [ 1.4] [ 1.8] [ 1.7] [ 0.4] [ 0.5] [ 0.7] [ 0.6] Fees Panel E. Incentive Fees Yes 1.72 0.50 0.86 -0.28 0.86 0.78 1.76 0.74 0.67 -0.39 1.09 1.12 No 1.42 0.22 0.96 -0.17 0.46 0.39 1.51 0.41 0.83 -0.27 0.68 0.68

Yes-No [ 1.7] [ 1.6] [-1.0] [-1.6] [ 1.7] [ 1.8] [ 0.8] [ 1.0] [-0.5] [-0.5] [ 0.8] [ 0.9]